Machines in minds toreverse engineer themachine that is mind     Randal A. Koene     Carboncopies.org                     ...
DYSTOPIA           2
SELECTION            3
EMPATHY          4
CHALLENGES             5
AUGMENTED            6
BEING        7
SUBSTRATE-INDEPENDENTMINDS                 WHOLE                  BRAIN              EMULATION8
9
ROADMAPREQUIREMENTS               10
SCOPERESOLUTION             11
EMULATIONPLATFORM    80 million ATP per action            potential            Brain: 20-40W (20-44% of            body)  ...
STRUCTURALCONNECTOME             13
14
15
FUNCTIONALCHARACTERIZATION                   16
MOLECULARTICKER TAPE      Kording (Northwestern), Boyden (MIT), Church (Harvard), Koene   17
MACHINESIN MINDS           Gomez-Martinez et al. (2009)                                    18
19
TEAMNETWORK          2um   1um                      20
Thank You    carboncopies.orgrandal.a.koene@carboncopies.org                                  21
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Machines in Minds to Reverse engineer the Machine that is Mind

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TEDxTallinn 2012 talk by Dr. Randal A. Koene. The talk has also been published as an article under the title "A Window of Opportunity". For more information see http://carboncopies.org/a-window-of-opportunity

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  • General concept of demux tree Specific implementation via nanowires, Rudolpho Llinas NYU School of Med, 500nm polymer or platinum wires
  • General concept of demux tree Specific implementation via nanowires, Rudolpho Llinas NYU School of Med, 500nm polymer or platinum wires
  • You choose a modeling resolution At it and below, functional characterization is key; the components must be simple enough to capture all their relevant behavior Above it, structural characterization is key; the interaction of components and their emergent product; connectome Function-structure in neuronal networks, and hardware/software – both are actually very similar in any other complex system, including computers You can use concurrent functional characterization and low-res structural information (e.g. finding all neurons) to infer functional connectivity – but there is risk of missing latent functions You can reduce the signal dimensions – considering only spatial architectural information (morphology) instead of spatial and temporal information (responses) by combining high-res structural information with inference of “functional type” - but even with a complete library of types and their paramter-related distributions of behaviors, there there is risk that mapping may not be one-to-one and that spatial data errors (e.g. misreadings of component sizes) may be cumulative and may be hard to correct without the addition of locally gathered functional information It is also better in general to rebuild a complex system bit by bit, while carrying out validation that it still works
  • Red blood cell diameter 8um. Existing biopassive & bioactive coatings for neural implants. Passes through every capillary to every neuron in the brain. Chip in cell has been done Artificial red blood cell has been done
  • Teams: Hub cell (8um), sensor, stimulation, herding, chains, tag delivery, morphology recording agents (1-2um)
  • Machines in Minds to Reverse engineer the Machine that is Mind

    1. 1. Machines in minds toreverse engineer themachine that is mind Randal A. Koene Carboncopies.org 1
    2. 2. DYSTOPIA 2
    3. 3. SELECTION 3
    4. 4. EMPATHY 4
    5. 5. CHALLENGES 5
    6. 6. AUGMENTED 6
    7. 7. BEING 7
    8. 8. SUBSTRATE-INDEPENDENTMINDS WHOLE BRAIN EMULATION8
    9. 9. 9
    10. 10. ROADMAPREQUIREMENTS 10
    11. 11. SCOPERESOLUTION 11
    12. 12. EMULATIONPLATFORM 80 million ATP per action potential Brain: 20-40W (20-44% of body) Average action potential takes 1.5ms About 4nW per event Supports 7 billion concurrent events Average rate 4-7Hz... 470 billion events/s 12
    13. 13. STRUCTURALCONNECTOME 13
    14. 14. 14
    15. 15. 15
    16. 16. FUNCTIONALCHARACTERIZATION 16
    17. 17. MOLECULARTICKER TAPE Kording (Northwestern), Boyden (MIT), Church (Harvard), Koene 17
    18. 18. MACHINESIN MINDS Gomez-Martinez et al. (2009) 18
    19. 19. 19
    20. 20. TEAMNETWORK 2um 1um 20
    21. 21. Thank You carboncopies.orgrandal.a.koene@carboncopies.org 21

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